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Results 1 to 25 of 1876

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On model selection in the computer ageHJORTH, U.Journal of statistical planning and inference. 1989, Vol 23, Num 1, pp 101-115, issn 0378-3758, 15 p.Article

The frequentist implications of optional stopping on Bayesian hypothesis testsSANBORN, Adam N; HILLS, Thomas T.Psychonomic bulletin & review. 2014, Vol 21, Num 2, pp 283-300, issn 1069-9384, 18 p.Article

Plateau-related summary statistics are uninformative for comparing working memory modelsVAN DEN BERG, Ronald; WEI JI MA.Attention, perception & psychophysics. 2014, Vol 76, Num 7, pp 2117-2135, issn 1943-3921, 19 p.Article

Bandlet image estimation with model selectionDOSSAL, Ch; LE PENNEC, E; MALLAT, S et al.Signal processing. 2011, Vol 91, Num 12, pp 2743-2753, issn 0165-1684, 11 p.Article

Multivariate regression model selection from small samples using Kullback's symmetric divergenceSEGHOUANE, Abd-Krim.Signal processing. 2006, Vol 86, Num 8, pp 2074-2084, issn 0165-1684, 11 p.Article

A note on overfitting properties of KIC and KICcSEGHOUANE, Abd-Krim.Signal processing. 2006, Vol 86, Num 10, pp 3055-3060, issn 0165-1684, 6 p.Article

Towards the systematic simplification of mechanistic modelsCOX, G. M; GIBBONS, J. M; WOOD, A. T. A et al.Ecological modelling. 2006, Vol 198, Num 1-2, pp 240-246, issn 0304-3800, 7 p.Article

Automatic basis selection techniques for RBF networksGHODSI, Ali; SCHUURMANS, Dale.Neural networks. 2003, Vol 16, Num 5-6, pp 809-816, issn 0893-6080, 8 p.Conference Paper

Analysis of some flexible families of distributions for estimation of wind speed distributionsUSTA, Ilhan; KANTAR, Yeliz Mert.Applied energy. 2012, Vol 89, Num 1, pp 355-367, issn 0306-2619, 13 p.Article

Special Issue on Model Selection and Optimization in Machine LearningÖZÖGÜR-AKYÜZ, Süreyya; ÜNAY, Devrim; SMOLA, Alex et al.Machine learning. 2011, Vol 85, Num 1-2, issn 0885-6125, 249 p.Serial Issue

Stochastic models for wind speed forecastingBIVONA, S; BONANNO, G; BURLON, R et al.Energy conversion and management. 2011, Vol 52, Num 2, pp 1157-1165, issn 0196-8904, 9 p.Article

Prior knowledge guided maximum expected likelihood based model selection and adaptation for nonnative speech recognitionXIAODONG HE; YUNXIN ZHAO.Computer speech & language (Print). 2007, Vol 21, Num 2, pp 247-265, issn 0885-2308, 19 p.Article

Reconstructing biochemical pathways from time course dataSRIVIDHYA, Jeyaraman; CRAMPIN, Edmund J; MCSHARRY, Patrick E et al.Proteomics (Weinheim. Print). 2007, Vol 7, Num 6, pp 828-838, issn 1615-9853, 11 p.Article

Influence analysis to assess sensitivity of the dropout processMOLENBERGHS, Geert; VERBEKE, Geert; THIJS, Herbert et al.Computational statistics & data analysis. 2001, Vol 37, Num 1, pp 93-113, issn 0167-9473Article

In-sample Model Selection for Trimmed Hinge Loss Support Vector MachineANGUITA, Davide; GHIO, Alessandro; ONETO, Luca et al.Neural processing letters. 2012, Vol 36, Num 3, pp 275-283, issn 1370-4621, 9 p.Article

An optimal adjustment procedure to minimize experiment time in decisions with multiple alternativesHAWKINS, Guy E; BROWN, Scott D; STEYVERS, Mark et al.Psychonomic bulletin & review. 2012, Vol 19, Num 2, pp 339-348, issn 1069-9384, 10 p.Article

Using priors to formalize theory: Optimal attention and the generalized context modelVANPAEMEL, Wolf; LEE, Michael D.Psychonomic bulletin & review. 2012, Vol 19, Num 6, pp 1047-1056, issn 1069-9384, 10 p.Article

On the number of groups in clusteringFISCHER, Aurélie.Statistics & probability letters. 2011, Vol 81, Num 12, pp 1771-1781, issn 0167-7152, 11 p.Article

Improved penalization for determining the number of factors in approximate factor modelsALESSI, Lucia; BARIGOZZI, Matteo; CAPASSO, Marco et al.Statistics & probability letters. 2010, Vol 80, Num 23-24, pp 1806-1813, issn 0167-7152, 8 p.Article

Differentiating between coefficient break and volatility breakFUKUDA, Kosei.Applied mathematics and computation. 2006, Vol 176, Num 1, pp 262-269, issn 0096-3003, 8 p.Article

Detecting multiple change-points in the mean of Gaussian process by model selectionLEBARBIER, E.Signal processing. 2005, Vol 85, Num 4, pp 717-736, issn 0165-1684, 20 p.Article

Bayesian model selection for mining mass spectrometry dataSAKSENA, Anshu; LUCARELLI, Dennis; WANG, I.-Jeng et al.Neural networks. 2005, Vol 18, Num 5-6, pp 843-849, issn 0893-6080, 7 p.Conference Paper

Learning kernel logistic regression in the presence of class label noiseBOOTKRAJANG, Jakramate; KABAN, Ata.Pattern recognition. 2014, Vol 47, Num 11, pp 3641-3655, issn 0031-3203, 15 p.Article

Optimized Parameter Search for Large Datasets of the Regularization Parameter and Feature Selection for Ridge RegressionBUTENEERS, Pieter; CALUWAERTS, Ken; DAMBRE, Joni et al.Neural processing letters. 2013, Vol 38, Num 3, pp 403-416, issn 1370-4621, 14 p.Article

A sparse version of the ridge logistic regression for large-scale text categorizationASEERVATHAM, Sujeevan; ANTONIADIS, Anestis; GAUSSIER, Eric et al.Pattern recognition letters. 2011, Vol 32, Num 2, pp 101-106, issn 0167-8655, 6 p.Article

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